docstr: Test the classifier in a finetune evaluation setting.
  
  The test verifies that the model and classification head are updated.
docstr_tok: ["Test","the","classifier","in","a","finetune","evaluation","setting",".","The","test","verifies","that","the","model","and","classification","head","are","updated","."]
code: def test__finetune(self) -> None:
      """Test the classifier in a finetune evaluation setting.
  
      The test verifies that the model and classification head are updated.
      """
      torch.manual_seed(0)
      dataset = FakeData(
          size=10,
          image_size=(3, 8, 8),
          num_classes=5,
          transform=ToTensor(),
      )
      train_dataloader = DataLoader(dataset, batch_size=2)
      val_dataloader = DataLoader(dataset, batch_size=2)
      linear = nn.Linear(3 * 8 * 8, 4)
      model = nn.Sequential(nn.Flatten(), linear)
      initial_weights = linear.weight.clone()
      linear_classifier = LinearClassifier(
          model=model,
          batch_size_per_device=2,
          feature_dim=4,
          num_classes=5,
          freeze_model=False,  # Don't freeze the model for finetuning.
      )
      initial_head_weights = (
          linear_classifier.classification_head.weight.clone()
      )
      trainer = Trainer(max_epochs=1, accelerator="cpu", devices=1)
      trainer.fit(linear_classifier, train_dataloader, val_dataloader)
      assert trainer.callback_metrics["train_loss"].item() > 0
      assert trainer.callback_metrics["train_top1"].item() >= 0
      assert (
          trainer.callback_metrics["train_top5"].item()
          >= trainer.callback_metrics["train_top1"].item()
      )
      assert trainer.callback_metrics["train_top5"].item() <= 1
      assert trainer.callback_metrics["val_loss"].item() > 0
      assert trainer.callback_metrics["val_top1"].item() >= 0
      assert (
          trainer.callback_metrics["val_top5"].item()
          >= trainer.callback_metrics["val_top1"].item()
      )
      assert trainer.callback_metrics["val_top5"].item() <= 1
  
      # Verify that weights were updated.
      assert not torch.all(torch.eq(initial_weights, linear.weight))
      # Verify that head weights were updated.
      assert not torch.all(
          torch.eq(
              initial_head_weights,
              linear_classifier.classification_head.weight,
          )
      )
  
file_path: lightly/tests/utils/benchmarking/test_linear_classifier.py
imports: import torch
  from pytorch_lightning import Trainer
  from torch import nn
  from torch.utils.data import DataLoader
  from torchvision.datasets import FakeData
  from torchvision.transforms import ToTensor
  from lightly.utils.benchmarking import LinearClassifier
label_desc: Use image node_id 1 for calling the TestLinearClassifier obj's underlying member method code with example usage: obj.test__finetune() without return types